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Titre : Introduction to Time Series and Forecasting Type de document : Monographie Auteurs : Peter J. Brockwell, Auteur ; Richard A. Davis, Auteur Editeur : Springer International Publishing Année de publication : 2016 Importance : 425 p. Format : 21 x 28 cm ISBN/ISSN/EAN : 978-3-319-29854-2 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes IGN] analyse spectrale
[Termes IGN] calcul matriciel
[Termes IGN] matrice de covariance
[Termes IGN] méthode du maximum de vraisemblance (estimation)
[Termes IGN] modèle de simulation
[Termes IGN] modèle stochastique
[Termes IGN] série temporelle
[Termes IGN] variable aléatoireRésumé : (éditeur) This book is aimed at the reader who wishes to gain a working knowledge of time series and forecasting methods as applied to economics, engineering and the natural and social sciences. It assumes knowledge only of basic calculus, matrix algebra and elementary statistics. This third edition contains detailed instructions for the use of the professional version of the Windows-based computer package ITSM2000, now available as a free download from the Springer Extras website. The logic and tools of time series model-building are developed in detail. Numerous exercises are included and the software can be used to analyze and forecast data sets of the user's own choosing. The book can also be used in conjunction with other time series packages such as those included in R. The programs in ITSM2000 however are menu-driven and can be used with minimal investment of time in the computational details. The core of the book covers stationary processes, ARMA and ARIMA processes, multivariate time series and state-space models, with an optional chapter on spectral analysis. Many additional special topics are also covered. Note de contenu : 1- Introduction
2- Stationary Processes
3- ARMA Models
4- Spectral Analysis
5- Modeling and Forecasting with ARMA Processes
6- Nonstationary and Seasonal Time Series Models
7- Time Series Models for Financial Data
8- Multivariate Time Series
9- State-Space Models
10- Forecasting Techniques
11- Further TopicsNuméro de notice : 25750 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE Nature : Monographie En ligne : https://link.springer.com/book/10.1007%2F978-3-319-29854-2 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94942 On estimation of the diagonal elements of a sparse precision matrix / Samuel Balmand in Electronic Journal of Statistics, vol 10 n° 1 (January 2016)
[article]
Titre : On estimation of the diagonal elements of a sparse precision matrix Type de document : Article/Communication Auteurs : Samuel Balmand , Auteur ; Arnak Dalalyan, Auteur Année de publication : 2016 Article en page(s) : pp 1551 - 1579 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes IGN] calcul matriciel
[Termes IGN] estimateur
[Termes IGN] matrice creuse
[Termes IGN] matrice de covariance
[Termes IGN] matrice diagonale
[Termes IGN] méthode du maximum de vraisemblance (estimation)
[Termes IGN] régression linéaire
[Termes IGN] résiduRésumé : (Auteur) In this paper, we present several estimators of the diagonal elements of the inverse of the covariance matrix, called precision matrix, of a sample of independent and identically distributed random vectors. The main focus is on the case of high dimensional vectors having a sparse precision matrix. It is now well understood that when the underlying distribution is Gaussian, the columns of the precision matrix can be estimated independently form one another by solving linear regression problems under sparsity constraints. This approach leads to a computationally efficient strategy for estimating the precision matrix that starts by estimating the regression vectors, then estimates the diagonal entries of the precision matrix and, in a final step, combines these estimators for getting estimators of the off-diagonal entries. While the step of estimating the regression vector has been intensively studied over the past decade, the problem of deriving statistically accurate estimators of the diagonal entries has received much less attention. The goal of the present paper is to fill this gap by presenting four estimators —that seem the most natural ones— of the diagonal entries of the precision matrix and then performing a comprehensive empirical evaluation of these estimators. The estimators under consideration are the residual variance, the relaxed maximum likelihood, the symmetry-enforced maximum likelihood and the penalized maximum likelihood. We show, both theoretically and empirically, that when the aforementioned regression vectors are estimated without error, the symmetry-enforced maximum likelihood estimator has the smallest estimation error. However, in a more realistic setting when the regression vector is estimated by a sparsity-favoring computationally efficient method, the qualities of the estimators become relatively comparable with a slight advantage for the residual variance estimator. Numéro de notice : A2016--107 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1214/16-EJS1148 Date de publication en ligne : 31/05/2016 En ligne : http://dx.doi.org/10.1214/16-EJS1148 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84707
in Electronic Journal of Statistics > vol 10 n° 1 (January 2016) . - pp 1551 - 1579[article]Documents numériques
en open access
A2016--107_On_estimation_of_the_diagonal_elements_of_a_sparse_precision_matrix.pdfAdobe Acrobat PDF
Titre : Statistical learning from a regression perspective Type de document : Guide/Manuel Auteurs : Richard A. Berk, Auteur Editeur : Springer International Publishing Année de publication : 2016 ISBN/ISSN/EAN : 978-3-319-44048-4 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes IGN] analyse de données
[Termes IGN] arbre aléatoire
[Termes IGN] classification et arbre de régression
[Termes IGN] ensachage
[Termes IGN] régression
[Termes IGN] régression multivariée par spline adaptative
[Termes IGN] régression par quantile
[Termes IGN] séparateur à vaste margeRésumé : (éditeur) This textbook considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response. As a first approximation, this can be seen as an extension of nonparametric regression. This fully revised new edition includes important developments over the past 8 years. Consistent with modern data analytics, it emphasizes that a proper statistical learning data analysis derives from sound data collection, intelligent data management, appropriate statistical procedures, and an accessible interpretation of results. A continued emphasis on the implications for practice runs through the text. Among the statistical learning procedures examined are bagging, random forests, boosting, support vector machines and neural networks. Response variables may be quantitative or categorical. As in the first edition, a unifying theme is supervised learning that can be treated as a form of regression analysis. Key concepts and procedures are illustrated with real applications, especially those with practical implications. A principal instance is the need to explicitly take into account asymmetric costs in the fitting process. For example, in some situations false positives may be far less costly than false negatives. Also provided is helpful craft lore such as not automatically ceding data analysis decisions to a fitting algorithm. In many settings, subject-matter knowledge should trump formal fitting criteria. Yet another important message is to appreciate the limitation of one’s data and not apply statistical learning procedures that require more than the data can provide. The material is written for upper undergraduate level and graduate students in the social and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems. The author uses this book in a course on modern regression for the social, behavioral, and biological sciences. Intuitive explanations and visual representations are prominent. All of the analyses included are done in R with code routinely provided. Note de contenu : 1- Statistical Learning as a Regression Problem
2- Splines, Smoothers, and Kernels
3- Classification and Regression Trees (CART)
4- Bagging
5- Random Forests
6- Boosting
7- Support Vector Machines
8- Some Other Procedures Briefly
9- Broader Implications and a Bit of Craft LoreNuméro de notice : 25800 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE Nature : Manuel de cours DOI : 10.1007/978-3-319-44048-4 En ligne : https://doi.org/10.1007/978-3-319-44048-4 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95043 Recommendations for the use of tree models to estimate national forest biomass and assess their uncertainty / Matieu Henry in Annals of Forest Science, vol 72 n° 6 (September 2015)
[article]
Titre : Recommendations for the use of tree models to estimate national forest biomass and assess their uncertainty Type de document : Article/Communication Auteurs : Matieu Henry, Auteur ; Miguel Cifuentes Jara, Auteur ; Maxime Réjou-Méchain, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 769 - 777 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes IGN] arbre (flore)
[Termes IGN] biomasse
[Termes IGN] disponibilité des données
[Termes IGN] estimation statistique
[Termes IGN] incertitude des données
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] inventaire forestier national (données France)
[Termes IGN] masse végétale
[Termes IGN] sylviculture
[Termes IGN] volume (grandeur)Résumé : (auteur) Key message : Three options are proposed to improve the accuracy of national forest biomass estimates and decrease the uncertainty related to tree model selection depending on available data and national contexts.
Introduction : Different tree volume and biomass equations result in different estimates. At national scale, differences of estimates can be important while they constitute the basis to guide policies and measures, particularly in the context of climate change mitigation.
Method : Few countries have developed national tree volume and biomass equation databases and have explored its potential to decrease uncertainty of volume and biomasttags estimates. With the launch of the GlobAllomeTree webplatform, most countries in the world could have access to country-specific databases. The aim of this article is to recommend approaches for assessing tree and forest volume and biomass at national level with the lowest uncertainty. The article highlights the crucial need to link allometric equation development with national forest inventory planning efforts.
Results : Models must represent the tree population considered. Data availability; technical, financial, and human capacities; and biophysical context, among other factors, will influence the calculation process.
Conclusion : Three options are proposed to improve accuracy of national forest assessment depending on identified contexts. Further improvements could be obtained through improved forest stratification and additional non-destructive field campaigns.Numéro de notice : A2015-410 Affiliation des auteurs : non IGN Thématique : FORET/MATHEMATIQUE Nature : Article DOI : 10.1007/s13595-015-0465-x Date de publication en ligne : 20/03/2015 En ligne : https://doi.org/10.1007/s13595-015-0465-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76898
in Annals of Forest Science > vol 72 n° 6 (September 2015) . - pp 769 - 777[article]
Titre : Bayesian methods for statistical analysis Type de document : Guide/Manuel Auteurs : Borek Puza, Auteur Editeur : ANNU Press Année de publication : 2015 ISBN/ISSN/EAN : 978-1-921934-26-1 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes IGN] analyse de données
[Termes IGN] inférence statistique
[Termes IGN] méthode de Monte-CarloRésumé : (Editeur) It's a book on statistical methods for analysing a wide variety of data. The book consists of 12 chapters, starting with basic concepts and covering numerous topics, including Bayesian estimation, decision theory, prediction, hypothesis testing, hierarchical models, Markov chain Monte Carlo methods, finite population inference, biased sampling and nonignorable nonresponse. The book contains many exercises, all with worked solutions, including complete computer code. It is suitable for self-study or a semester-long course, with three hours of lectures and one tutorial per week for 13 weeks. Note de contenu :
Chapter 1: Bayesian Basics Part 1
Chapter 2: Bayesian Basics Part 2
Chapter 3: Bayesian Basics Part 3
Chapter 4: Computational Tools
Chapter 5: Monte Carlo Basics
Chapter 6: MCMC Methods Part 1
Chapter 7: MCMC Methods Part 2
Chapter 8: Inference via WinBUGS
Chapter 9: Bayesian Finite Population Theory
Chapter 10: Normal Finite Population Models
Chapter 11: Transformations and Other Topics
Chapter 12: Biased Sampling and Nonresponse
Appendix A: Additional Exercises
Appendix B: Distributions and Notation
Appendix C: Abbreviations and AcronymsNuméro de notice : 22715 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE Nature : Manuel En ligne : http://www.doabooks.org/doab?func=search&uiLanguage=en&template=&query=978192193 [...] Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85226 Documents numériques
en open access
22715_Bayesian_methods_for_statistical_analysis.pdfAdobe Acrobat PDF Magic square of real spectral and time series analysis with an application to moving average processes / I. Krasbutter (2015)PermalinkPermalinkPermalinkPermalinkLASSO-type estimators for semiparametric nonlinear mixed-effects models estimation / Ana Arribas-Gil in Statistics and Computing, vol 24 n° 3 (May 2014)PermalinkPermalinkPermalinkProbabilités pour les sciences de l'ingénieur / Manuel Samuelides (2014)PermalinkOn the formulation of the alternative hypothesis for geodetic outlier detection / Rüdiger Lehmann in Journal of geodesy, vol 87 n° 4 (April 2013)PermalinkPermalink